Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pantheon-org/tekhne --skill notebooklmgit clone --depth 1 https://github.com/pantheon-org/tekhneWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pantheon-org/tekhne/notebooklm)<a href="https://agentmods.dev/skills/pantheon-org/tekhne/notebooklm"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/notebooklm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pantheon-org/tekhne/notebooklm"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/notebooklm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01951 |
| Opus 5 | $0.00017 | $0.00975 |
| Sonnet 5 | $0.00007 | $0.00390 |
| Haiku 4.5 | $0.00003 | $0.00195 |
Grade A, and why
notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NotebookLM Research Assistant
Query Google NotebookLM to get Gemini's source-grounded answers from your uploaded documents. Each question opens a fresh browser session, retrieves the answer exclusively from your knowledge base, and closes.
When to Use This Skill
Trigger when the user:
- Mentions NotebookLM explicitly
- Shares a NotebookLM URL (
https://notebooklm.google.com/notebook/...) - Asks to query their notebooks or personal documentation
- Wants to add documentation to the NotebookLM library
- Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"
Mindset
NotebookLM is a grounding layer, not a search engine. Three principles govern every interaction:
- Ground every answer in sources — answers come exclusively from uploaded documents; never synthesise from general knowledge when querying a notebook.
- Never guess notebook content — use Smart Add (query first) to discover what a notebook contains before labelling it.
- Follow up until complete — one query is rarely enough; check every answer for gaps and issue follow-ups before synthesising for the user.
When Not to Use This Skill
- The user needs general web search or LLM knowledge — use standard Claude responses instead
- No NotebookLM URL or notebook ID is available and authentication is not yet set up
- The user wants data analysis or code generation — NotebookLM is for document Q&A only
- The target content is not uploaded to any NotebookLM notebook
⚠️ CRITICAL: Add Command — Smart Discovery
When adding a notebook without full details, query first to discover content:
# Step 1: Query the notebook about its content
python ./scripts/run.py ask_question.py \
--question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" \
--notebook-url "[URL]"
# Step 2: Use discovered information to add it
python ./scripts/run.py notebook_manager.py add \
--url "[URL]" \
--name "[Based on content]" \
--description "[Based on content]" \
--topics "[Based on content]"
What ships with it
23 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .audits/2026-04-10/analysis.md 1.2 KB
- .audits/2026-04-10/audit.json 494 B
- .audits/2026-04-10/remediation-plan.md 2.9 KB
- .audits/latest 10 B
- evals/instructions.json 1.2 KB
- evals/scenario-01.md 2.1 KB
- evals/scenario-02.md 2.1 KB
- evals/scenario-03.md 2.3 KB
- references/api_reference.md 7.4 KB
- references/troubleshooting.md 8.5 KB
- references/usage_patterns.md 9.3 KB
- requirements.txt 326 B
- scripts/__init__.py 2.6 KB runs code
- scripts/ask_question.py 15 KB runs code
- scripts/auth_manager.py 12 KB runs code
- scripts/browser_session.py 8.9 KB runs code
- scripts/browser_utils.py 3.7 KB runs code
- scripts/cleanup_manager.py 9.6 KB runs code
- scripts/config.py 1.2 KB runs code
- scripts/logger.py 4.5 KB runs code
- scripts/notebook_manager.py 14 KB runs code
- scripts/run.py 2.9 KB runs code
- scripts/setup_environment.py 7.0 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 222 lines · 34 tokens per session scan A a927cc0fd49e
notebooklm is a skill published in the GitHub repository pantheon-org/tekhne (10 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,951 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
officecli-word-form
Use this skill to create fillable Word forms (.docx) with real Content Controls (SDT) + legacy FormField checkboxes + MERGEFIELD mail-merge placeholders + document protection. Trigger on: 'fillable form', 'form fields', 'content controls', 'SDT', 'word form', 'fill in', 'only editable fields', 'protect document'…
officecli-academic-paper
Use this skill to build academic-style .docx output: journal / conference / thesis chapters carrying formal citation style (APA, Chicago, IEEE, MLA), numbered equations, figure & table cross-references, footnotes/endnotes, bibliography, or multi-column journal layout. Trigger on: 'research paper', 'journal paper'…
officecli-data-dashboard
Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to…
A set of instructions for working with PDF files, which are documents designed to preserve their layout across devices.
officecli
Create, analyze, proofread, and modify Office documents (.docx, .xlsx, .pptx) using the officecli CLI tool. Use when the user wants to create, inspect, check formatting, find issues, add charts, or modify Office documents.
mxl-decompile
A 1C tool that converts an existing tabular document layout (MXL) from Template.xml into a JSON layout definition. The JSON uses the same format accepted by the corresponding layout compiler.